Whether it’s transaction data, survey responses, social media posts, or chatbot interactions, companies receive a deluge of data on a daily basis. While some of it—primarily quantitative data—is stored and structured in databases, nearly 90% of business data is unstructured. This type of information can be difficult for companies to get their arms around; unlike quantitative data, unstructured or qualitative data is disorganized and spread across various sources, making it hard to analyze using traditional data analysis strategies.
However, hidden in these stacks of emails, meeting transcripts, online reviews, and social media comments are valuable insights that your business could leverage to improve its products and services. But with so much data to sort through, how can you make sense of it all?
That’s where AI tools that leverage large language models (LLMs) like GPT-4 come into play. After uncovering key themes and trends from huge stacks of text, these tools can help you unlock the “why” behind your unstructured data.
Let’s break down what unstructured data is, how to collect it, and why AI-powered tools are helping companies unlock valuable customer insights from it.
Unstructured data is any kind of information that isn’t organized into a database or spreadsheet. This includes emails, customer feedback surveys, audio recordings (like phone calls), videos, images—basically anything that isn’t a traditional form like a report or table. Unlike structured data, which follows a predetermined format, unstructured data doesn’t adhere to any specific standard, making it more difficult to analyze.
Most organizations receive an overwhelming amount of unstructured data each day, but struggle to make use of it. However, with the help of advanced data analytics technologies like natural language processing (NLP), companies can uncover the valuable insights hidden in these massive data sets. Doing so can inform better decision making around marketing, product features, and more.
Since most businesses generate large amounts of unstructured data every day, it’s become increasingly important for companies to understand it on a deeper level. Those that overlook this type of data risk missing out on key insights that can help them make better decisions.
One especially relevant area for unstructured data is understanding consumer sentiment. While common metrics like net promoter score (NPS) and customer satisfaction score (CSAT) are useful for understanding how customers feel about your business, there’s a treasure trove of data beyond these high-level benchmarks. For most companies, the majority of customers don’t respond to surveys, meaning their feedback often goes unheard. But they can leave a trail of unstructured feedback in the form of chatbot interactions, product reviews, and social media posts. Today’s generative analysis tools are able to gather that data and uncover the sentiment behind it, uncovering new insights about your customers beyond survey data.
In addition to understanding how customers feel about their business, unstructured data can reveal what a company’s target audience is looking for in its products or services. Hidden in sources such as purchasing trends, forums, and customer service calls are patterns and commonalities in sentiment. Companies can use these findings to adjust their existing offerings to better meet customer needs, and anticipate what they might want from future products.
AI-powered tools enable businesses to unlock the potential hidden within their vast stores of unstructured data. By helping them identify patterns within text such as customer conversations, they can understand their customers on a new level that helps them make better marketing and product decisions.
For example, sentiment analysis algorithms can be used to improve customer retention by responding to their concerns quickly. If the AI finds that a particular customer is unhappy, it can trigger a chatbot interaction that asks them how the company can resolve their issue. Since the AI already has a good understanding of the source of the customer’s frustration, it can recommend specific resolutions to their problem. In the best case scenario, this can actually turn detractors into promoters.
But dealing with complaints isn’t all these tools can do—they can also help you prioritize new product features that your customers are likely to enjoy. By looking at customer reviews across a multitude of channels, modern AI tools can see which features are complained about the most, and which ones customers love. Product managers can take this feedback into consideration when rolling out updates or launching future products.
These are just some of the ways AI-powered tools can help you unlock the “why” by helping to analyze your unstructured data. Companies that get the most out of their unstructured data will gain an edge by understanding their customers on a level like never before.
Analyzing unstructured data is becoming an increasingly important part of doing business today. Companies are seeking ways to extract value from their existing resources without investing too much time or money into manual processes like transcription or sentiment analysis.
Fortunately, AI-powered tools now exist that make it easy for businesses to organize their troves of unstructured content quickly and accurately. With these types of automated tools at your disposal, you’re able to find needles in the haystack in real-time without having to manually comb through endless amounts of text-based data.
Want to see how investing in such technologies could open up new opportunities for your business? Test our AI with your data.